OpenRaiser/Intern-Atlas huggingface dataset:
Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter
Venue: arXiv cs.CL 2020
Year: 2020

Doctors and nurses in these weeks are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. Arguably the discourse around the current epidemic will make use of war-related metaphors too,not only in public discourse and the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a corpus of 200k tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY literal frame covers a wider portion of the corpus, among the figurative framings WAR is the most frequently used, and thus arguably the most conventional one. However, we conclude, this frame is not apt to elaborate the discourse around many aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options, or a metaphor menu, may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and ideas during the current pandemic.
Investigating usability of mobile health applications in Bangladesh
Venue: arXiv cs.HC 2020
Year: 2020

Background: Lack of usability can be a major barrier for the rapid adoption of mobile services. Therefore, the purpose of this paper is to investigate the usability of Mobile Health applications in Bangladesh. Method: We followed a 3-stage approach in our research. First, we conducted a keyword-based application search in the popular app stores. We followed the affinity diagram approach and clustered the found applications into nine groups. Second, we randomly selected four apps from each group (36 apps in total) and conducted a heuristic evaluation. Finally, we selected the highest downloaded app from each group and conducted user studies with 30 participants. Results: We found 61% usability problems are catastrophe or major in nature from heuristic inspection. The most (21%) violated heuristic is aesthetic and minimalist design. The user studies revealed low System Usability Scale (SUS) scores for those apps that had a high number of usability problems based on the heuristic evaluation. Thus, the results of heuristic evaluation and user studies complement each other. Conclusion: Overall, the findings suggest that the usability of the mobile health apps in Bangladesh is not satisfactory in general and could be a potential barrier for wider adoption of mobile health services.
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
Venue: arXiv cs.LG 2020
Year: 2020

Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs. However, CapsNets require extremely intense computations and are difficult to be deployed in their original form at the resource-constrained edge devices. This paper makes the first attempt to quantize CapsNet models, to enable their efficient edge implementations, by developing a specialized quantization framework for CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet model for the CIFAR10 dataset, the framework reduces the memory footprint by 6.2x, with only 0.15% accuracy loss. We will open-source our framework at https://git.io/JvDIF in August 2020.
Hiring Fairly in the Age of Algorithms
Venue: arXiv cs.HC 2020
Year: 2020

Widespread developments in automation have reduced the need for human input. However, despite the increased power of machine learning, in many contexts these programs make decisions that are problematic. Biases within data and opaque models have amplified human prejudices, giving rise to such tools as Amazon's (now defunct) experimental hiring algorithm, which was found to consistently downgrade resumes when the word "women's" was added before an activity. This article critically surveys the existing legal and technological landscape surrounding algorithmic hiring. We argue that the negative impact of hiring algorithms can be mitigated by greater transparency from the employers to the public, which would enable civil advocate groups to hold employers accountable, as well as allow the U.S. Department of Justice to litigate. Our main contribution is a framework for automated hiring transparency, algorithmic transparency reports, which employers using automated hiring software would be required to publish by law. We also explain how existing regulations in employment and trade secret law can be extended by the Equal Employment Opportunity Commission and Congress to accommodate these reports.
An algorithmic framework for colouring locally sparse graphs
Venue: arXiv cs.DS 2020
Year: 2020

We develop an algorithmic framework for graph colouring that reduces the problem to verifying a local probabilistic property of the independent sets. With this we give, for any fixed $k\ge 3$ and $\varepsilon>0$, a randomised polynomial-time algorithm for colouring graphs of maximum degree $Δ$ in which each vertex is contained in at most $t$ copies of a cycle of length $k$, where $1/2\le t\le Δ^\frac{2\varepsilon}{1+2\varepsilon}/(\logΔ)^2$, with $\lfloor(1+\varepsilon)Δ/\log(Δ/\sqrt t)\rfloor$ colours. This generalises and improves upon several notable results including those of Kim (1995) and Alon, Krivelevich and Sudakov (1999), and more recent ones of Molloy (2019) and Achlioptas, Iliopoulos and Sinclair (2019). This bound on the chromatic number is tight up to an asymptotic factor $2$ and it coincides with a famous algorithmic barrier to colouring random graphs.
Energy Efficient and Delay Aware Vehicular Edge Cloud
Venue: arXiv cs.NI 2020
Year: 2020

Vehicular Edge Clouds (VECs) is a new distributed processing paradigm that exploits the revolution in the processing capabilities of vehicles to offer energy efficient services and improved QoS. In this paper we tackle the problem of processing allocation in a cloud-fog-VEC architecture by developing a joint optimization Mixed Integer Linear Programming (MILP) model to minimize power consumption, propagation delay, and queuing delay. The results show that while VEC processing can reduce the power consumption and propagation delay, VEC processing can increase the queuing delay because of the low data rate connectivity between the vehicles and the data source nodes.
Sampling Rates for $\ell^1$-Synthesis
Venue: arXiv cs.IT 2020
Year: 2020

This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian measurements under the assumption of a synthesis-based sparsity model. Solving the $\ell^1$-synthesis basis pursuit allows for a simultaneous estimation of a coefficient representation as well as the sought-for signal. However, due to linear dependencies within redundant dictionary atoms it might be impossible to identify a specific representation vector, although the actual signal is still successfully recovered. The present manuscript studies both estimation problems from a non-uniform, signal-dependent perspective. By utilizing recent results on the convex geometry of linear inverse problems, the sampling rates describing the phase transitions of each formulation are identified. In both cases, they are given by the conic Gaussian mean width of an $\ell^1$-descent cone that is linearly transformed by the dictionary. In general, this expression does not allow a simple calculation by following the polarity-based approach commonly found in the literature. Hence, two upper bounds involving the sparsity of coefficient representations are provided: The first one is based on a local condition number and the second one on a geometric analysis that makes use of the thinness of high-dimensional polyhedral cones with not too many generators. It is furthermore revealed that both recovery problems can differ dramatically with respect to robustness to measurement noise -- a fact that seems to have gone unnoticed in most of the related literature. All insights are carefully undermined by numerical simulations.
Effective Removal of Operational Log Messages: an Application to Model Inference
Venue: arXiv cs.SE 2020
Year: 2020

Model inference aims to extract accurate models from the execution logs of software systems. However, in reality, logs may contain some "noise" that could deteriorate the performance of model inference. One form of noise can commonly be found in system logs that contain not only transactional messages---logging the functional behavior of the system---but also operational messages---recording the operational state of the system (e.g., a periodic heartbeat to keep track of the memory usage). In low-quality logs, transactional and operational messages are randomly interleaved, leading to the erroneous inclusion of operational behaviors into a system model, that ideally should only reflect the functional behavior of the system. It is therefore important to remove operational messages in the logs before inferring models. In this paper, we propose LogCleaner, a novel technique for removing operational logs messages. LogCleaner first performs a periodicity analysis to filter out periodic messages, and then it performs a dependency analysis to calculate the degree of dependency for all log messages and to remove operational messages based on their dependencies. The experimental results on two proprietary and 11 publicly available log datasets show that LogCleaner, on average, can accurately remove 98% of the operational messages and preserve 81% of the transactional messages. Furthermore, using logs pre-processed with LogCleaner decreases the execution time of model inference (with a speed-up ranging from 1.5 to 946.7 depending on the characteristics of the system) and significantly improves the accuracy of the inferred models, by increasing their ability to accept correct system behaviors (+43.8 pp on average, with pp=percentage points) and to reject incorrect system behaviors (+15.0 pp on average).
Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration
Venue: arXiv cs.RO 2020
Year: 2020

We address the problem of maintaining resource availability in a networked multi-robot team performing distributed tracking of unknown number of targets in an environment of interest. Based on our model, robots are equipped with sensing and computational resources enabling them to cooperatively track a set of targets in an environment using a distributed Probability Hypothesis Density (PHD) filter. We use the trace of a robot's sensor measurement noise covariance matrix to quantify its sensing quality. While executing the tracking task, if a robot experiences sensor quality degradation, then robot team's communication network is reconfigured such that the robot with the faulty sensor may share information with other robots to improve the team's target tracking ability without enforcing a large change in the number of active communication links. A central system which monitors the team executes all the network reconfiguration computations. We consider two different PHD fusion methods in this paper and propose four different Mixed Integer Semi-Definite Programming (MISDP) formulations (two formulations for each PHD fusion method) to accomplish our objective. All four MISDP formulations are validated in simulation.
Embracing Companion Technologies
Venue: arXiv cs.HC 2020
Year: 2020

As an increasing number of interactive devices offer human-like assistance, there is a growing need to understand the human experience of interactive agents. When interactive artefacts with human-like features become intertwined in our everyday experience, we need to make sure that they assume the right roles and contribute to our wellbeing. In this theoretical exploration, we propose a reframing of our understanding of interactions with everyday technologies by proposing the metaphor of digital companions. We employ the theory in the philosophy of empathy to propose a framework for understanding how users develop relationships with digital agents. The experiential framework for companion technologies provides connections between the users' psychological needs and companion-like features of interactive systems. Our work provides a theoretical basis for rethinking the user experience of everyday artefacts with a humanistic mindset and poses future challenges for HCI.
Using Player's Body-Orientation to Model Pass Feasibility in Soccer
Venue: arXiv cs.CV 2020
Year: 2020

Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Orientation data is gathered from body pose estimations that are properly projected onto the 2D game field; moreover, a geometrical solution is provided, through the definition of a feasibility measure, to determine which players are better oriented towards each other. Once analyzed more than 6000 pass events, results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the orientation feasibility measure with the recently introduced Expected Possession Value metric is studied; promising results are obtained, thus showing that existing models can be refined by using orientation as a key feature. These models could help both coaches and analysts to have a better understanding of the game and to improve the players' decision-making process.
Locality Sensitive Hashing for Set-Queries, Motivated by Group Recommendations
Venue: arXiv cs.DS 2020
Year: 2020

Locality Sensitive Hashing (LSH) is an effective method to index a set of points such that we can efficiently find the nearest neighbors of a query point. We extend this method to our novel Set-query LSH (SLSH), such that it can find the nearest neighbors of a set of points, given as a query. Let $ s(x,y) $ be the similarity between two points $ x $ and $ y $. We define a similarity between a set $ Q$ and a point $ x $ by aggregating the similarities $ s(p,x) $ for all $ p\in Q $. For example, we can take $ s(p,x) $ to be the angular similarity between $ p $ and $ x $ (i.e., $1-{\angle (x,p)}/π$), and aggregate by arithmetic or geometric averaging, or taking the lowest similarity. We develop locality sensitive hash families and data structures for a large set of such arithmetic and geometric averaging similarities, and analyze their collision probabilities. We also establish an analogous framework and hash families for distance functions. Specifically, we give a structure for the euclidean distance aggregated by either averaging or taking the maximum. We leverage SLSH to solve a geometric extension of the approximate near neighbors problem. In this version, we consider a metric for which the unit ball is an ellipsoid and its orientation is specified with the query. An important application that motivates our work is group recommendation systems. Such a system embeds movies and users in the same feature space, and the task of recommending a movie for a group to watch together, translates to a set-query $ Q $ using an appropriate similarity.
Secure protocol to protect location privacy in distance calculation
Venue: arXiv cs.CR 2020
Year: 2020

Several applications require computing distances between different people. For example,this is required if we want to obtain the close contacts of people in case of and epidemic,or when restraining orders are imposed by a judge. However, periodically revealing location might pose a privacy threat to the involved parties. Continuous location data may be used to infer personal information about the owner, like behaviors, religious beliefs, buying habits, routines, etc. In this paper, we show that it is possible to calculate distance between two parties without disclosing their latitude and longitude data. For this purpose, we design a secure protocol based on the ElGamal cryptosystem and its homomorphic properties. The proposed protocol allows the calculation of distances while preserving location privacy. The protocol is analyzed in terms of security and performance. The security analysis shows that no involved party can learn any information about location.
Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation
Venue: arXiv cs.CL 2020
Year: 2020

Lack of specialized data makes building a multi-domain neural machine translation tool challenging. Although emerging literature dealing with low resource languages starts to show promising results, most state-of-the-art models used millions of sentences. Today, the majority of multi-domain adaptation techniques are based on complex and sophisticated architectures that are not adapted for real-world applications. So far, no scalable method is performing better than the simple yet effective mixed-finetuning, i.e finetuning a generic model with a mix of all specialized data and generic data. In this paper, we propose a new training pipeline where knowledge distillation and multiple specialized teachers allow us to efficiently finetune a model without adding new costs at inference time. Our experiments demonstrated that our training pipeline allows improving the performance of multi-domain translation over finetuning in configurations with 2, 3, and 4 domains by up to 2 points in BLEU.
A Study on the Challenges of Using Robotics Simulators for Testing
Venue: arXiv cs.RO 2020
Year: 2020

Robotics simulation plays an important role in the design, development, and verification and validation of robotic systems. Recent studies have shown that simulation may be used as a cheaper, safer, and more reliable alternative to manual, and widely used, process of field testing. This is particularly important in the context of continuous integration pipelines, where integrated automated testing is key to reducing costs while maintaining system safety. However, simulation and automated testing are not seeing the degree of widespread adoption in practice that their potential would motivate. Our goal in this paper is to develop a principled understanding of the ways developers use simulation in their process, and the challenges they face in doing so. This type of understanding can guide the development of more effective simulators and testing techniques for modern robotics development. To that end, we conduct a survey of 82 robotics developers from a diversity of backgrounds that addresses the current capabilities and limits of simulation technology in practice. We find that simulation is used by 85% of our participants for testing, and that many participants desire to use simulation as part of their test automation. We identify 10 high-level challenges that impede developers from using simulation for manual and automated testing, and general purposes. These challenges include the gap between simulation and reality, a lack of reproducibility, and considerable resource costs associated with using simulators. Finally, we outline avenues for improvement in the development of new simulators that can help simulation reach its potential as a means of verification and validation.
On the computability of continuous maximum entropy distributions with applications
Venue: arXiv cs.DS 2020
Year: 2020

We initiate a study of the following problem: Given a continuous domain $Ω$ along with its convex hull $\mathcal{K}$, a point $A \in \mathcal{K}$ and a prior measure $μ$ on $Ω$, find the probability density over $Ω$ whose marginal is $A$ and that minimizes the KL-divergence to $μ$. This framework gives rise to several extremal distributions that arise in mathematics, quantum mechanics, statistics, and theoretical computer science. Our technical contributions include a polynomial bound on the norm of the optimizer of the dual problem that holds in a very general setting and relies on a "balance" property of the measure $μ$ on $Ω$, and exact algorithms for evaluating the dual and its gradient for several interesting settings of $Ω$ and $μ$. Together, along with the ellipsoid method, these results imply polynomial-time algorithms to compute such KL-divergence minimizing distributions in several cases. Applications of our results include: 1) an optimization characterization of the Goemans-Williamson measure that is used to round a positive semidefinite matrix to a vector, 2) the computability of the entropic barrier for polytopes studied by Bubeck and Eldan, and 3) a polynomial-time algorithm to compute the barycentric quantum entropy of a density matrix that was proposed as an alternative to von Neumann entropy in the 1970s: this corresponds to the case when $Ω$ is the set of rank one projections matrices and $μ$ corresponds to the Haar measure on the unit sphere. Our techniques generalize to the setting of Hermitian rank $k$ projections using the Harish-Chandra-Itzykson-Zuber formula, and are applicable even beyond, to adjoint orbits of compact Lie groups.
ACDC-Tracing: Towards Anonymous Citizen-Driven Contact Tracing
Venue: arXiv cs.CY 2020
Year: 2020

As we enter the control phase of the COVID-19 pandemic, many efforts have been dedicated to developing smartphone-based contact tracing apps in order to automatically identify people that a person with COVID-19 might have infected. These applications while potentially useful, present significant adoption, societal, technical and privacy challenges. We propose ACDC-Tracing, a simpler, anonymous, voucher-based contact tracing solution that relies on peoples' knowledge of their own close contacts. People who test positive are given an anonymous voucher which they can share with a limited number of people whom they think they might be infected. The recipients can use this voucher to book a COVID-19 test and can receive their test results without ever revealing their identity. People receiving positive result are given vouchers to further backtrack the path of infection. This is a fully anonymous solution which does not require any sharing of location data, Bluetooth, or having an app installed on people's mobile device. Moreover, ACDC-Tracing can be tested for effectiveness at a small scale without requiring adoption by the entire population, which would enable acquiring fast evidence about its efficacy and scalability. Finally, it is compatible with and complementary to alternative approaches to contact tracing.